| plotFitCurve {DEqMS} | R Documentation |
This function is to plot the fitted prior variance against the number of quantified peptides/PSMs.
plotFitCurve(fit,fit.method="loess",type="boxplot",xlab="",main="")
fit |
an list object produced by spectraCounteBayes function |
fit.method |
the method used to fit prior variance against the number of peptides. default loess. |
type |
an character indicating the type of plot to be generated. options are boxplot and scatterplot. default is boxplot |
xlab |
the title for x axis |
main |
the title for the figure |
return a plot graphic
Yafeng Zhu
library(ExperimentHub)
eh = ExperimentHub()
query(eh, "DEqMS")
dat.psm = eh[["EH1663"]]
dat.psm.log = dat.psm
dat.psm.log[,3:12] = log2(dat.psm[,3:12])
dat.gene.nm = medianSweeping(dat.psm.log,group_col = 2)
psm.count.table = as.data.frame(table(dat.psm$gene)) # generate PSM count table
rownames(psm.count.table)=psm.count.table$Var1
cond = c("ctrl","miR191","miR372","miR519","ctrl",
"miR372","miR519","ctrl","miR191","miR372")
sampleTable <- data.frame(
row.names = colnames(dat.psm)[3:12],
cond = as.factor(cond)
)
gene.matrix = as.matrix(dat.gene.nm)
design = model.matrix(~cond,sampleTable)
fit1 <- eBayes(lmFit(gene.matrix,design))
# add PSM count for each gene
fit1$count <- psm.count.table[rownames(fit1$coefficients),2]
fit2 = spectraCounteBayes(fit1)
plotFitCurve(fit2,type="boxplot",xlab="PSM count",
main="TMT data PXD004163")